The neural recordings known as Local Field Potentials (LFPs) provide important information on how neural circuits operate and relate. Due to the involvement of complex electronic apparatuses in the recording setups, these signals are often significantly contaminated by artifacts generated by a number of internal and external sources. To make the best use of these signals, it is imperative to detect and remove the artifacts from these signals. Hence, this work proposes a pattern recognition neural network based single-channel automatic artifact detection tool. The tool is capable of detecting the artifacts with an 93.2% of overall accuracy and requires an average computing time of 2.57 seconds to analyse LFPs of one minute duration, making it a strong candidate for online deployment without the need for employing high performance computing equipment.

Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes

The increment of communication technologies and the development of signal processing require efficient identification techniques for communication radiation. However, the complex characteristics of electromagnetic environment are not adequately handled by linear methods. Since the fractal theory is well suited for nonlinear problems, the relation of self-similarity (SS) between baseband and modulated signals is studied in this paper. Indeed, the existence of SS’s relation between the baseband and modulated signals is proved and verified under certain conditions. Finally, the analysis and extraction of individual signal fingerprint features in communication radiation source is constructed. Experimental results show that the proposed property SS is effective in the identification and classification of communication radiation sources.

Property of Self-Similarity Between Baseband and Modulated Signals

Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform discharges, where an unsupervised learning exploiting the EEG signal physiological characteristics and smooth nonlinear energy operator (SNEO) based on the K-means clustering has been firstly proposed. Multiple statistical EEG features derived from the frontal electrodes and other electrodes are then extracted to characterize eye blink artifacts. Discriminative feature selection scheme based on the variance filtering and Relief algorithms has been respectively studied, and the average correlation coefficient (ACC) is applied for feature optimization evaluation. The eye blink artifact detection is finally achieved based on the support vector machine (SVM) trained on the optimized EEG features. The effectiveness of the proposed algorithm is demonstrated by experiments carried out on the EEG database of 11 subjects recorded from the Children’s Hospital, Zhejiang University School of Medicine (CHZU). Comparisons to several state-of-the-art (SOTA) eye blink artifact detection methods are also presented.

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Eye Blink Artifact Detection With Novel Optimized Multi-Dimensional Electroencephalogram Features

A brain-actuated wheelchair can be used to aid the movement of differentially enabled communities who face much difficulties while commuting from one place to another. In this research work, the active brain signals emanated from subjects while performing four different kinesthetic motor imagery tasks are recorded using Electroencephalography (EEG). Three different feature sets, namely, Fractal Dimension (FD), Mel-Frequency Cepstral Coefficients (MFCCs) and combined features of FD with MFCCs are extracted from the recorded EEG signals. The extracted features are then associated to classify the type of motor imagery tasks and three feedforward multi-layer Perceptrons trained with Levenberg-Marquardt method are developed. The performance of the three features are evaluated in term of classification rate and compared. Simple Elman network and NARX network models are then developed using the extracted features and evaluated. From the results, it is observed that the Elman network model trained with combined features of FD with MFCCs has yielded a higher classification accuracy for all the 5 subjects in the range of 98.98-100percent. The obtained result clearly indicates that the Elman network and combined features of FD with MFCCs has potential to classify the four different motor imagery tasks.

Motor-Imagery Task Classification using Mel-Cepstral and Fractal Fusion based Features

Electroencephalogram (EEG) is used to measure the bioelectric potential on the brain scalp. The recorded EEG signal can have different types of artifacts and the interpretation of a noisy EEG signal is difficult. In this research work, a simple method is proposed to minimize the artifacts present in the EEG signals recorded while perceiving a pure tone. The recorded EEG signal can contain artifacts, such as movement artifacts, muscle contraction artifacts and saturation artifacts. In the proposed method, fractal dimension based features with different interval length and time-domain based energy features were extracted from the EEG signals with and without simulated noise. Using the extracted features, neural network models were developed to classify the EEG signal as a normal or a noisy signal. Further, the performance of the model is also evaluated in terms of classification rate. From the results, it is observed that the neural network model developed with the combined fractal dimension features of interval length 2,3,4,5 and 6 with frame size 128 has the highest classification accuracy of 95.5%.

Yogesh C K

Papers

Fractal feature based detection of muscular and ocular artifacts in EEG signals